from gevent import pywsgi import sys import time import argparse import uvicorn from typing import Union from pydantic import BaseModel from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch import os import openedai import numpy as np import asyncio from urllib.parse import urlparse import nacos import configparser app = openedai.OpenAIStub() moderation = None device = "cuda" if torch.cuda.is_available() else "cpu" #device = "cpu" labels = ['hate', 'hate_threatening', 'harassment', 'harassment_threatening', 'self_harm', 'self_harm_intent', 'self_harm_instructions', 'sexual', 'sexual_minors', 'violence', 'violence_graphic', ] label2id = {l:i for i, l in enumerate(labels)} id2label = {i:l for i, l in enumerate(labels)} model_name = "duanyu027/moderation_0628" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels),id2label=id2label, label2id=label2id, problem_type = "multi_label_classification") model.to(device) model.eval() #model = torch.quantization.quantize_dynamic( # model, {torch.nn.Linear}, dtype=torch.qint8 #) torch.set_num_threads(1) def register_service(client,service_name,service_ip,service_port,cluster_name,health_check_interval,weight,http_proxy,domain,protocol,direct_domain): try: # 初始化 metadata metadata = {} # 如果 http_proxy 为 True，添加额外的 metadata 键值对 if http_proxy: metadata["http_proxy"] = True if direct_domain: metadata["domain"] = f"{protocol}://{service_ip}:{service_port}" else: metadata["domain"] = f"{domain}/port/{service_port}" else: metadata["http_proxy"] = False metadata["domain"] = f"{protocol}://{service_ip}:{service_port}" response = client.add_naming_instance( service_name, service_ip, service_port, cluster_name, weight, metadata, enable=True, healthy=True, ephemeral=True, heartbeat_interval=health_check_interval ) return response except Exception as e: print(f"Error registering service to Nacos: {e}") return True class ModerationsRequest(BaseModel): model: str = "text-moderation-latest" # or "text-moderation-stable" input: Union[str, list[str]] @app.on_event("startup") def startup_event(): # 创建配置解析器 config = configparser.ConfigParser() # 读取配置文件 if not config.read('config.ini'): raise RuntimeError("配置文件不存在") # Nacos server and other configurations NACOS_SERVER = config['nacos']['nacos_server'] NAMESPACE = config['nacos']['namespace'] CLUSTER_NAME = config['nacos']['cluster_name'] client = nacos.NacosClient(NACOS_SERVER, namespace=NAMESPACE, username=config['nacos']['username'], password=config['nacos']['password']) SERVICE_NAME = config['nacos']['service_name'] HEALTH_CHECK_INTERVAL = int(config['nacos']['health_check_interval']) WEIGHT = int(config.get('nacos', 'weight', fallback='1')) HTTP_PROXY = config.getboolean('server', 'http_proxy') DOMAIN = config['server']['domain'] PROTOCOL = config['server']['protocol'] DIRECT_DOMAIN = config.getboolean('server', 'direct_domain') # Parse AutoDLServiceURL autodl_url = os.environ.get('AutoDLServiceURL') if not autodl_url: raise RuntimeError("Error: AutoDLServiceURL environment variable is not set.") parsed_url = urlparse(autodl_url) SERVICE_IP = parsed_url.hostname SERVICE_PORT = parsed_url.port if not SERVICE_IP or not SERVICE_PORT: raise RuntimeError("Error: Invalid AutoDLServiceURL format.") # Register service with Nacos if not register_service(client, SERVICE_NAME, SERVICE_IP, SERVICE_PORT, CLUSTER_NAME, HEALTH_CHECK_INTERVAL, WEIGHT, HTTP_PROXY, DOMAIN, PROTOCOL, DIRECT_DOMAIN): raise RuntimeError("Service is healthy but failed to register.") @app.post("/v1/moderations") async def moderations(request: ModerationsRequest): results = { "id": f"modr-{int(time.time()*1e9)}", "model": "text-moderation-005", "results": [], } if isinstance(request.input, str): request.input = [request.input] thresholds = { "sexual": 0.5, "hate": 0.5, "harassment": 0.5, "self_harm": 0.5, "sexual_minors": 0.9, "hate_threatening": 0.9, "violence_graphic": 0.9, "self_harm_intent": 0.9, "self_harm_instructions": 0.9, "harassment_threatening": 0.9, "violence": 0.5, } for text in request.input: predictions = await predict(text, model, tokenizer) category_scores = {labels[i]: predictions[0][i].item() for i in range(len(labels))} detect = {key: score > thresholds[key] for key, score in category_scores.items()} detected = any(detect.values()) results['results'].append({ 'flagged': detected, 'categories': detect, 'category_scores': category_scores, }) return results def sigmoid(x): return 1/(1 + np.exp(-x)) def parse_args(argv): parser = argparse.ArgumentParser(description='Moderation API') parser.add_argument('--host', type=str, default='0.0.0.0') parser.add_argument('--port', type=int, default=5002) parser.add_argument('--test-load', action='store_true') return parser.parse_args(argv) async def predict(text, model, tokenizer): encoding = tokenizer.encode_plus( text, return_tensors='pt' ) input_ids = encoding['input_ids'].to(device) attention_mask = encoding['attention_mask'].to(device) # 运行模型预测在独立的线程中 def _predict(): with torch.no_grad(): outputs = model(input_ids, attention_mask=attention_mask) return torch.sigmoid(outputs.logits) loop = asyncio.get_running_loop() predictions = await loop.run_in_executor(None, _predict) # 清理 GPU 内存 del input_ids del attention_mask torch.cuda.empty_cache() return predictions # Main if __name__ == "__main__": uvicorn.run("moderations:app", host="0.0.0.0", port=6006, reload=True)